Finite Element Model for Concrete Slab-Column Connections with Shear Reinforcement
Bibliographic record
Abstract
Much of the current code provisions for designing slab-column connections against punching shear are based on empirically derived formulations based on tests of partial scale isolated slab-column specimens. Although many experiments have been conducted on shear reinforced concrete flat slabs supported on columns, due to cost or time constraints, there are still many parameters that have not been adequately studied in the laboratory. These tests can be supplemented by analytical results of properly calibrated nonlinear finite element analysis (NLFEA) to enhance the existing experimental database and formulate rational design recommendations for future codes. This paper presents a rational approach for calibrating an NLFEA model in ABAQUS using interior and edge slab-column connection specimens. The calibration includes a study to determine how to effectively model the shear reinforcement and shear reinforced area. This further includes a detailed analysis of the modeling of the shear reinforcing elements to ensure appropriate rotational capability of the shear reinforced region of the slab without significantly reducing the predicted capacity of the model. The calibrated models show good agreement with test data based on load-deflection, moment-curvature, and bolt strain behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".